hits_at_n_score¶

ampligraph.evaluation.
hits_at_n_score
(ranks, n)¶ Hits@N
The function computes how many elements of a vector of rankings
ranks
make it to the topn
positions.It can be used in conjunction with the learning to rank evaluation protocol of
ampligraph.evaluation.evaluate_performance()
.It is formally defined as follows:
\[Hits@N = \sum_{i = 1}^{Q} 1 \, if rank_{(s, p, o)_i} \leq N\]where \(Q\) is a set of triples and \((s, p, o)\) is a triple \(\in Q\).
Consider the following example. Each of the two positive triples identified by
*
are ranked against four corruptions each. When scored by an embedding model, the first triple ranks 2nd, and the other triple ranks first. Hits@1 and Hits@3 are:s p o score rank Jack born_in Ireland 0.789 1 Jack born_in Italy 0.753 2 * Jack born_in Germany 0.695 3 Jack born_in China 0.456 4 Jack born_in Thomas 0.234 5 s p o score rank Jack friend_with Thomas 0.901 1 * Jack friend_with China 0.345 2 Jack friend_with Italy 0.293 3 Jack friend_with Ireland 0.201 4 Jack friend_with Germany 0.156 5 Hits@3=1.0 Hits@1=0.5
Parameters:  ranks (ndarray, shape [n]) – Input ranks of n positive statements.
 n (int) – The maximum rank considered to accept a positive.
Returns: hits_n_score – The Hits@n score
Return type: float
Examples
>>> import numpy as np >>> from ampligraph.evaluation.metrics import hits_at_n_score >>> rankings = np.array([1, 12, 6, 2]) >>> hits_at_n_score(rankings, n=3) 0.5